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In my last article, I suggested that the sudden appearance of a cluster of articles on specific investment topics might be a precursor to significant trends that might deserve attention. At the time I had dividend investing in mind.

Since then, Morningstar has published two articles, the first, pointing out the virtues of investing in low volatility stocks, and the second advertising a cluster of new low volatility ETFs.

Thinking that this might be telling me something, I decided to add a volatility filter to the selection criteria as a possible means of improving the portfolio selection process.

An Interesting Road Travelled

As it turns out, there is a lot more to the subject than volatility. It appears that a body of work is forming around the concept of measuring both volatility and correlation. This addresses the observation that, in times of stress, historical correlations change, and incorporating both measures may improve active portfolio management.

The methodology incorporates the calculation of the Mahalanobis Distance (MD) between assets considered. Sudden changes in MD are easily detected and should attract attention. Coincident changes over many assets may suggest systemic risk.

I wanted to see what this looks like and so compared the MD for a steady dividend stock, such as Consolidated Edison (NYSE:ED) against the S&P 500. The result is:

[Click to enlarge all images]

And then against a more volatile, blue chip stock such as Caterpillar (NYSE:CAT):

These results indicate that Caterpillar and the S&P 500 (NYSEARCA:SPY) spike at the same time, and in this period at least, Caterpillar moves further. No matter how good a stock Caterpillar may be, it is substantially exposed to market risk.

Enhanced Methodology

The analysis of Caterpillar above matches my experiences, which are that returns from momentum (or higher beta) investing are at significant risk during market downturns. Once the MD starts to spike, which mostly reflects a bad period in the market, stocks with Caterpillar's MD characteristics do not perform well. If a portfolio were to contain many stocks with similar characteristics, there would be a significant risk of substantial underperformance.

Solutions to this problem that suggest themselves are;

  • Change tack towards low volatility investing (which is where we came in) or,
  • Look for portfolios where the bumps in MD are not significantly aligned in terms of time or amplitude or,
  • Modify the bets in portfolio construction so as to avoid stocks with riskier MD profiles.

It should also be noted that the market MD profile has its own characteristic, and there are significant periods of time when there is no systemic downside to protect against. In these periods, go momentum and go hard. In my view however, the next few months do contain risks, and therefore I am willing to add an MD screen, as per points 2 and 3 above, to the next round of portfolio suggestions.

Lower Volatility Portfolio

In forming a portfolio modified for low volatility (LVA), I only selected stocks that were part of a performing sector. This is to avoid as much as possible the “shooting stars”, stocks which can’t sustain price appreciation in a weak business environment. We are still after consistency.

All other selection criteria are the same as for previous portfolios, and the MD filter is added at the end to identify potential problem stocks. The performance relative to our previous Alternative A portfolio is:

And the stocks comprising the Low Vol portfolio are:

In my view, the current earnings profile for these stocks support future consistent performance, however we are all too aware that eanings surprises are likely over the next month, and these should be monitored closely.


In his latest post on SA, Jeff Miller starts with, Most investors do not know how to measure risk. Usually they do not even try”.

My hope is that the work outlined above, which refers to advanced concepts of risk, will lead to value added decision-making. For those interested, I urge them to Google Kritzman, M. and Y. Li. “Skulls, Financial Turbulence, and Risk Management.” The Financial Analysts Journal, May/June 2010.

This is the source reference, and it leads to a number of interesting papers on portfolio management. While the math is not difficult, it is nevertheless a challenge to organise a system that can be applied to thousands of stocks.

Comparing to a Low Vol ETF

We started with a reference to Low Volatility ETF’s. PowerShares S&P 500 Low Volatility Portfolio ETF (NYSEARCA:SPLV) is one of these products and my understanding is that it comprises the following as the top 10 holdings.

Comparing our selections in LVA to the performance of 10 stocks above, in an equal weighted portfolio, we get the following result.

Disclaimer: The content in this document is provided as general information only and should not be taken as investment advice. The contents in this explanatory paper shall not be construed as a recommendation to buy or sell any security or financial product, or to participate in any particular trading or investment strategy. The ideas expressed on this site are solely the opinions of the author. The author may or may not have a position in any security referenced herein. Any action that you take as a result of information or analysis on this site is ultimately your responsibility.

Source: Consistent Alpha: Taking Volatility Into Account